针对医疗电子设备锂电池不确定性发生故障耽误病人救治的问题,提出了一套医疗电子设备锂电池故障预测与健康管理系统(Prognostics and Health Management-PHM);搭建了一套医疗电子设备锂电池数据测试与退化状态模拟的实验平台;为了反映...针对医疗电子设备锂电池不确定性发生故障耽误病人救治的问题,提出了一套医疗电子设备锂电池故障预测与健康管理系统(Prognostics and Health Management-PHM);搭建了一套医疗电子设备锂电池数据测试与退化状态模拟的实验平台;为了反映医疗电子设备锂电池健康状态,将锂电池四个健康因子作为医疗电子设备锂电池退化状态的特征进行提取,并通过非线性自回归(Nonlinear Autogressive with Exogenous Inputs-NARX)神经网络,对四个健康因子的数据进行训练,训练后用于容量估计,得出等间隔放电时间序列能够较好地表征锂电池健康状态;为了提高基本粒子滤波算法(Particle Filter-PF)的精度从而更精确地预测锂电池剩余寿命(Remaing Useful Life-RUL),通过人工免疫粒子滤波算法(Artificial Immune Particle FilterAIPF)与经验模型对锂电池进行剩余寿命预测,并将PF预测的结果与AIPF预测的结果进行对比,发现AIPF预测更加准确,说明AIPF有效抑制了PF重采样过程中粒子退化问题,验证了医疗电子设备锂电池故障预测与健康管理系统的可行性与可实施性。展开更多
When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the feature...When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.展开更多
文摘针对医疗电子设备锂电池不确定性发生故障耽误病人救治的问题,提出了一套医疗电子设备锂电池故障预测与健康管理系统(Prognostics and Health Management-PHM);搭建了一套医疗电子设备锂电池数据测试与退化状态模拟的实验平台;为了反映医疗电子设备锂电池健康状态,将锂电池四个健康因子作为医疗电子设备锂电池退化状态的特征进行提取,并通过非线性自回归(Nonlinear Autogressive with Exogenous Inputs-NARX)神经网络,对四个健康因子的数据进行训练,训练后用于容量估计,得出等间隔放电时间序列能够较好地表征锂电池健康状态;为了提高基本粒子滤波算法(Particle Filter-PF)的精度从而更精确地预测锂电池剩余寿命(Remaing Useful Life-RUL),通过人工免疫粒子滤波算法(Artificial Immune Particle FilterAIPF)与经验模型对锂电池进行剩余寿命预测,并将PF预测的结果与AIPF预测的结果进行对比,发现AIPF预测更加准确,说明AIPF有效抑制了PF重采样过程中粒子退化问题,验证了医疗电子设备锂电池故障预测与健康管理系统的可行性与可实施性。
基金National Natural Science Foundation of China(Nos.61662070,61363059)Youth Science Fund Project of Lanzhou Jiaotong University(No.2018036)。
文摘When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.
